class_docstr: Class for huggingface models that implement the .generate method.
  
  Attributes:
      *args and **kwargs are passed to HFModel's __init__
docstr: Generate according to the model's generate method.
docstr_tok: ["Generate","according","to","the","model","'s","generate","method","."]
code: def generate(self, text, variant="seq2seq", **kwargs):
      """
      Generate according to the model's generate method.
      """
      DEFAULT_MIN_LENGTH = 10
      DEFAULT_MAX_LENGTH = 20
  
      if text == "":
          raise ValueError("Some text must be provided")
  
      # Get and remove do_sample or set to False
      do_sample = kwargs.pop("do_sample", None) or False
      params = [
          "temperature",
          "top_k",
          "top_p",
          "repetition_penalty",
          "length_penalty",
          "num_beams",
          "num_return_sequences",
          "num_generations",
      ]
  
      # If params are changed, we want to sample
      for param in params:
          if param in kwargs.keys() and kwargs[param] != None:
              do_sample = True
              break
  
      if "temperature" in kwargs:
          # No sampling
          if kwargs["temperature"] == 0.0:
              do_sample = False
              del kwargs["temperature"]
  
      # Override, name correctly
      if "num_generations" in kwargs:
          if kwargs["num_generations"] != None:
              kwargs["num_return_sequences"] = kwargs["num_generations"]
  
          del kwargs["num_generations"]
  
      min_length = kwargs.pop("min_length", DEFAULT_MIN_LENGTH)
      max_length = kwargs.pop("max_length", DEFAULT_MAX_LENGTH)
  
      if min_length is None:
          min_length = DEFAULT_MIN_LENGTH
  
      if max_length is None:
          max_length = DEFAULT_MAX_LENGTH
  
      is_list = False
      if isinstance(text, list):
          is_list = True
  
      if not is_list:
          text = [text]
  
      all_tokens = []
      output = []
      for text in text:
          features = self.tokenizer(text, return_tensors="pt")
  
          input_length = len(features["input_ids"][0])
  
          for k, v in features.items():
              features[k] = v.to(self._model_device)
  
          if variant == "causal_lm":
              min_length += input_length
              max_length += input_length
  
          with torch.no_grad():
              tokens = self.model.generate(
                  do_sample=do_sample,
                  min_length=min_length,
                  max_length=max_length,
                  **features,
                  **kwargs,
              )
  
          if variant == "causal_lm":
              output.append(
                  [
                      self.tokenizer.decode(
                          tokens[input_length:],
                          skip_special_tokens=True,
                      )
                      for tokens in tokens
                  ]
              )
          else:
              output.append(
                  [
                      self.tokenizer.decode(
                          tokens, skip_special_tokens=True
                      )
                      for tokens in tokens
                  ]
              )
  
      # Unwrap generation list
      if kwargs.get("num_return_sequences", 1) == 1:
          output_unwrapped = []
          for value in output:
              output_unwrapped.append(value[0])
  
          output = output_unwrapped
  
      # Return single item
      if not is_list:
          output = output[0]
  
      return output
  
imports: from typing import List, Tuple, Dict
  from transformers import AutoModelForPreTraining, AutoTokenizer, AutoModelForSequenceClassification, AdamW
  from torch.utils.data import DataLoader, Subset
  from sentence_transformers import SentenceTransformer
  from functools import partial
  import os
  import torch
  import torch.nn.functional
  import pytorch_lightning
  from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  from pytorch_lightning.utilities.memory import garbage_collection_cuda
label_desc: Use image node_id 1 for calling the HFTextGenerationModel obj's underlying member method code with example usage: obj.generate(text, variant) and returns: output